# TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios

arXiv: https://arxiv.org/abs/2607.06179 · HTML: https://mlsanity.com/paper/2607.06179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 13 of 13)

- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation](https://mlsanity.com/paper/2407.05361/) (2024)
- [Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound](https://mlsanity.com/paper/2502.05139/) (2025)
- [NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations](https://mlsanity.com/paper/2508.04195/) (2025)
- [ATST: Audio Representation Learning with Teacher-Student Transformer](https://mlsanity.com/paper/2204.12076/) (2022)
- [A Scalable Pipeline for Enabling Non-Verbal Speech Generation and Understanding](https://mlsanity.com/paper/2508.05385/) (2025)
- [A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds](https://mlsanity.com/paper/2305.10729/) (2023)
- [Domestic activities clustering from audio recordings using convolutional capsule autoencoder network](https://mlsanity.com/paper/2105.03583/) (2021)
- [Leveraging Audio-Tagging Assisted Sound Event Detection using Weakified Strong Labels and Frequency Dynamic Convolutions](https://mlsanity.com/paper/2304.12688/) (2023)
- [Streaming Audio Transformers for Online Audio Tagging](https://mlsanity.com/paper/2305.17834/) (2023)
- [Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes](https://mlsanity.com/paper/2607.01297/) (2026)

## Abstract (arXiv)

There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an Automatic Audio Annotation Pipeline--TriA Pipeline, which can efficiently convert audio from various scenarios into high-quality training data with audio event annotations. A TriA dataset was constructed with the TriA Pipeline, over 2130 hours of audio covering 431 audio classes. Furthermore, we partitioned a prior-knowledge-guided subset (TriAGK) from TriA and conduct comparative experiments on three domestic AC tasks. Comparing the result on manually annotated data only and that on manually annotated data combines TriAGK, TriAGK could achieve average relative gains of 3.97% in accuracy and 3.35% in Macro-F1, validating the effectiveness of TriAGK and the TriA Pipeline.